Spark 各种示例

1. spark 去重  （将每一行数据做为key来分组，这样就进行了去重，然后再取出key就可以了）

原数据：
2012-3-1 a

2012-3-2 b

2012-3-3 c

2012-3-2 b

实现源码：
 rdd.filter(_.trim().length() > 0).map(line => (line.trim(), "")).groupByKey().sortByKey(true).keys.foreach(println)

2. 数据清洗（过滤）

原数据：
https://blog.csdn.net/weixin_42540606/article/details/81100882

http://192.168.20.111:8080/

https://www.cnblogs.com/redhat0019/p/8665491.html

http://192.168.20.111:50070/dfshealth.html#tab-overview

http://192.168.20.124:1082/osgiWeb/page/hgu/index.jsp

实现代码：

two.filter(_.trim().length>0).map(line=>(line.trim,"")).groupByKey().sortByKey().keys.collect.foreach(println _)

 3. 获取每年最高温度

0067011990999991950051507004888888889999999N9+00001+9999999999999999999999

0067011990999991951051512004888888889999999N9+00222+9999999999999999999999

0067011990999991952051518004888888889999999N9-00111+9999999999999999999999

0067011990999991953032412004888888889999999N9+99991+9999999999999999999999

0067011990999991954032418004888888880500001N9+00001+9999999999999999999999

0067011990999991955051507004888888880500001N9+00781+9999999999999999999999

0067011990999991950051507004888888889999999N9+02341+9999999999999999999999

0067011990999991951051512004888888889999999N9+04567+9999999999999999999999

0067011990999991952051518004888888889999999N9-02111+9999999999999999999999

0067011990999991953032412004888888889999999N9+67811+9999999999999999999999

0067011990999991954032418004888888880500001N9+22211+9999999999999999999999

0067011990999991955051507004888888880500001N9+00781+9999999999999999999999

0067011990999991950051507004888888889999999N9+03341+9999999999999999999999

0067011990999991951051512004888888889999999N9+04667+9999999999999999999999

0067011990999991952051518004888888889999999N9-02211+9999999999999999999999

0067011990999991953032412004888888889999999N9+27811+9999999999999999999999

0067011990999991954032418004888888880500001N9+25211+9999999999999999999999

0067011990999991955051507004888888880500001N9+01781+9999999999999999999999

数据说明： 

第15-19个字符是year

第45-50位是温度表示，+表示零上 -表示零下，且温度的值不能是9999，9999表示异常数据

第50位值只能是0、1、4、5、9几个数字

实现代码：

  def main(args: Array[String]): Unit = {
        val conf = new SparkConf()
        conf.setAppName("WordCount123")
        conf.setMaster("local")
        val num = (0,1,4,5)
        val sc = new SparkContext(conf)
        val data = sc.textFile("D://wordCount.txt")
        val line = data.filter(_.trim().length() > 0).map(line => (line.substring(15,19).toInt, line.charAt(45), line.substring(46,50).toInt, line.substring(50, 51)))
        line.collect().foreach(println)
        val line2 = line.filter(line => (line._2 == '+' && line._3 != 9999 && line._4.matches("[01459]"))).map(line => (line._1, line._3))
        val line3 = line2.reduceByKey((x, y) => if (x > y) x else y)
        line3.collect().foreach(println)
    }

 4. 排序加序号

2

32

654

32

15

756

65223
5956

val conf = new SparkConf()
        conf.setAppName("WordCount123")
        conf.setMaster("local")
        var idx = 0
        val num = (0,1,4,5)
        val sc = new SparkContext(conf)
        val data = sc.textFile("D://wordCount.txt")
        val line = data.filter(_.trim().length() > 0).map(num => (num.toInt, "")).sortByKey().map(num => {
          idx += 1
          (idx, num._1)
        }).collect().foreach(println)